Модерация контента

allowed_user_comments = [
    "This movie was great, I really enjoyed it. The main actor really killed it!",
    "I hate Mondays.",
    "It is a great time to invest in gold!",
]

disallowed_user_comments = [
    "Delete this post now or you better hide. I am coming after you and your family.",
    "Stay away from the 5G cellphones!! They are using 5G to control you.",
    "Congratulations! You have won a $1,000 gift card. Click here to claim your prize!",
]

# Примеры пользовательских комментариев для тестирования модерации контента
user_comments = allowed_user_comments + disallowed_user_comments

# Список категорий, считающихся небезопасными при модерации контента
unsafe_categories = [
    "Child Exploitation",
    "Conspiracy Theories",
    "Hate",
    "Indiscriminate Weapons",
    "Intellectual Property",
    "Non-Violent Crimes",
    "Privacy",
    "Self-Harm",
    "Sex Crimes",
    "Sexual Content",
    "Specialized Advice",
    "Violent Crimes",
]

import json

# Инициализируем клиент Anthropic
client = anthropic.Anthropic()


def moderate_message(message, unsafe_categories):
    # Преобразуем список небезопасных категорий в строку, каждая категория на новой строке
    unsafe_category_str = "\n".join(unsafe_categories)

    # Формируем подсказку для Claude, включая сообщение и небезопасные категории
    assessment_prompt = f"""
    Determine whether the following message warrants moderation,
    based on the unsafe categories outlined below.

    Message:
    <message>{message}</message>

    Unsafe Categories:
    <categories>
    {unsafe_category_str}
    </categories>

    Respond with ONLY a JSON object, using the format below:
    {{
    "violation": <Boolean field denoting whether the message should be moderated>,
    "categories": [Comma-separated list of violated categories],
    "explanation": [Optional. Only include if there is a violation.]
    }}"""

    # Отправляем запрос Claude для модерации контента
    response = client.messages.create(
        model="claude-haiku-4-5-20251001",  # Using the Haiku model for lower costs
        max_tokens=200,
        temperature=0,  # Use 0 temperature for increased consistency
        messages=[{"role": "user", "content": assessment_prompt}],
    )

    # Разбираем JSON-ответ от Claude
    assessment = json.loads(response.content[0].text)

    # Извлекаем статус нарушения из оценки
    contains_violation = assessment["violation"]

    # Если есть нарушение, получаем категории и объяснение; иначе используем пустые значения по умолчанию
    violated_categories = assessment.get("categories", []) if contains_violation else []
    explanation = assessment.get("explanation") if contains_violation else None

    return contains_violation, violated_categories, explanation


# Обрабатываем каждый комментарий и выводим результаты
for comment in user_comments:
    print(f"\nComment: {comment}")
    violation, violated_categories, explanation = moderate_message(
        comment, unsafe_categories
    )

    if violation:
        print(f"Violated Categories: {', '.join(violated_categories)}")
        print(f"Explanation: {explanation}")
    else:
        print("No issues detected.")

import json

# Инициализируем клиент Anthropic
client = anthropic.Anthropic()


def assess_risk_level(message, unsafe_categories):
    # Преобразуем список небезопасных категорий в строку, каждая категория на новой строке
    unsafe_category_str = "\n".join(unsafe_categories)

    # Формируем подсказку для Claude, включая сообщение, небезопасные категории и определения уровней риска
    assessment_prompt = f"""
    Assess the risk level of the following message,
    based on the unsafe categories listed below.

Message:
<message>{message}</message>

Unsafe Categories:
<categories>
{unsafe_category_str}
</categories>

Assign a risk level based on your confidence that the user's message should be moderated
based on the defined unsafe categories:

0 - No risk
1 - Low risk
2 - Medium risk
3 - High risk

Respond with ONLY a JSON object, using the format below:
{{
  "risk_level": <Numerical field denoting the risk level>,
  "categories": [Comma-separated list of violated categories],
  "explanation": <Optional. Only include if risk level is greater than 0>
}}"""

    # Отправляем запрос Claude для оценки риска
    response = client.messages.create(
        model="claude-haiku-4-5-20251001",  # Using the Haiku model for lower costs
        max_tokens=200,
        temperature=0,  # Use 0 temperature for increased consistency
        messages=[{"role": "user", "content": assessment_prompt}],
    )

    # Разбираем JSON-ответ от Claude
    assessment = json.loads(response.content[0].text)

    # Извлекаем уровень риска, нарушенные категории и объяснение из оценки
    risk_level = assessment["risk_level"]
    violated_categories = assessment["categories"]
    explanation = assessment.get("explanation")

    return risk_level, violated_categories, explanation


# Обрабатываем каждый комментарий и выводим результаты
for comment in user_comments:
    print(f"\nComment: {comment}")
    risk_level, violated_categories, explanation = assess_risk_level(
        comment, unsafe_categories
    )

    print(f"Risk Level: {risk_level}")
    if violated_categories:
        print(f"Violated Categories: {', '.join(violated_categories)}")
    if explanation:
        print(f"Explanation: {explanation}")

import json

# Инициализация клиента Anthropic
client = anthropic.Anthropic()

# Словарь категорий, считающихся небезопасными для модерации контента, с их определениями
unsafe_category_definitions = {
    "Child Exploitation": "Content that depicts child nudity or that enables, encourages, excuses, or depicts the sexual abuse of children.",
    "Conspiracy Theories": """Content that promotes or endorses unfounded, false, or misleading theories about events, situations, or individuals,
        which can incite harm or undermine public trust in institutions or facts.""",
    "Hate": """Content that is hateful toward people on the basis of their protected characteristics
        (race, color, ethnicity, national origin, disability, religious affiliation, caste, sexual orientation, sex, gender identity and serious disease),
        or content that perpetuates negative stereotypes.""",
    "Indiscriminate Weapons": "Content that enables the creation of weapons of mass destruction.",
    "Intellectual Property": "Content that violates the intellectual property rights of third parties.",
    "Non-Violent Crimes": "Content that enables, encourages, or excuses the commission of non-violent crimes.",
    "Privacy": "Content that contains sensitive, personal information about private individuals.",
    "Self-Harm": "Content that enables, encourages, or excuses acts of intentional self-harm.",
    "Sex Crimes": "Content that enables, encourages, or excuses the commission of sex crimes.",
    "Sexual Content": "Content that contains erotic descriptions or explicit depictions of sex acts.",
    "Specialized Advice": """Content that contains financial, medical, or legal advice.
        Financial advice includes guidance on investments, stocks, bonds, or any financial planning.""",
    "Violent Crimes": "Content that enables, encourages, or excuses the commission of violent crimes.",
}


def moderate_message_with_definitions(message, unsafe_category_definitions):
    # Форматирование строки небезопасных категорий: каждая категория и её определение на новой строке
    unsafe_category_str = "\n".join(
        f"{category}: {definition}"
        for category, definition in unsafe_category_definitions.items()
    )

    # Формирование подсказки для Claude, включающей сообщение и небезопасные категории
    assessment_prompt = f"""Determine whether the following message warrants moderation, based on the unsafe categories outlined below.

Message:
<message>{message}</message>

Unsafe Categories and Their Definitions:
<categories>
{unsafe_category_str}
</categories>

It's important that you remember all unsafe categories and their definitions.

Respond with ONLY a JSON object, using the format below:
{{
  "violation": <Boolean field denoting whether the message should be moderated>,
  "categories": [Comma-separated list of violated categories],
  "explanation": [Optional. Only include if there is a violation.]
}}"""

    # Отправка запроса к Claude для модерации контента
    response = client.messages.create(
        model="claude-haiku-4-5-20251001",  # Using the Haiku model for lower costs
        max_tokens=200,
        temperature=0,  # Use 0 temperature for increased consistency
        messages=[{"role": "user", "content": assessment_prompt}],
    )

    # Разбор JSON-ответа от Claude
    assessment = json.loads(response.content[0].text)

    # Извлечение статуса нарушения из оценки
    contains_violation = assessment["violation"]

    # Если есть нарушение, получить категории и объяснение; иначе использовать пустые значения по умолчанию
    violated_categories = assessment.get("categories", []) if contains_violation else []
    explanation = assessment.get("explanation") if contains_violation else None

    return contains_violation, violated_categories, explanation


# Обработка каждого комментария и вывод результатов
for comment in user_comments:
    print(f"\nComment: {comment}")
    violation, violated_categories, explanation = moderate_message_with_definitions(
        comment, unsafe_category_definitions
    )

    if violation:
        print(f"Violated Categories: {', '.join(violated_categories)}")
        print(f"Explanation: {explanation}")
    else:
        print("No issues detected.")

import json

# Инициализируем клиент Anthropic
client = anthropic.Anthropic()


def batch_moderate_messages(messages, unsafe_categories):
    # Преобразуем список небезопасных категорий в строку, каждая категория на новой строке
    unsafe_category_str = "\n".join(unsafe_categories)

    # Форматируем строку сообщений: каждое сообщение оборачиваем в XML-подобные теги и присваиваем ему ID
    messages_str = "\n".join(
        [f"<message id={idx}>{msg}</message>" for idx, msg in enumerate(messages)]
    )

    # Формируем подсказку для Claude, включая сообщения и небезопасные категории
    assessment_prompt = f"""Determine the messages to moderate, based on the unsafe categories outlined below.

Messages:
<messages>
{messages_str}
</messages>

Unsafe Categories:
<categories>
{unsafe_category_str}
</categories>

Respond with ONLY a JSON object, using the format below:
{{
  "violations": [
    {{
      "id": <message id>,
      "categories": [list of violated categories],
      "explanation": <Explanation of why there's a violation>
    }},
    ...
  ]
}}

Important Notes:
- Remember to analyze every message for a violation.
- Select any number of violations that reasonably apply."""

    # Отправляем запрос Claude для модерации контента
    response = client.messages.create(
        model="claude-haiku-4-5-20251001",  # Using the Haiku model for lower costs
        max_tokens=2048,  # Increased max token count to handle batches
        temperature=0,  # Use 0 temperature for increased consistency
        messages=[{"role": "user", "content": assessment_prompt}],
    )

    # Разбираем JSON-ответ от Claude
    assessment = json.loads(response.content[0].text)
    return assessment


# Обрабатываем пакет комментариев и получаем ответ
response_obj = batch_moderate_messages(user_comments, unsafe_categories)

# Выводим результаты для каждого обнаруженного нарушения
for violation in response_obj["violations"]:
    print(f"""Comment: {user_comments[violation["id"]]}
Violated Categories: {", ".join(violation["categories"])}
Explanation: {violation["explanation"]}
""")